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体验新版 GitCode,发现更多精彩内容 >>
提交
2a7bcfc8
编写于
4月 14, 2018
作者:
K
Kolesnikov Sergey
提交者:
Waleed
4月 16, 2018
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差异文件
loss weights
上级
6cfc657c
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
31 addition
and
13 deletion
+31
-13
mrcnn/config.py
mrcnn/config.py
+10
-0
mrcnn/model.py
mrcnn/model.py
+21
-13
未找到文件。
mrcnn/config.py
浏览文件 @
2a7bcfc8
...
@@ -164,6 +164,16 @@ class Config(object):
...
@@ -164,6 +164,16 @@ class Config(object):
# Weight decay regularization
# Weight decay regularization
WEIGHT_DECAY
=
0.0001
WEIGHT_DECAY
=
0.0001
# Loss weights for more precise optimization.
# Can be used for R-CNN training setup.
LOSS_WEIGHTS
=
{
"rpn_class_loss"
:
1.
,
"rpn_bbox_loss"
:
1.
,
"mrcnn_class_loss"
:
1.
,
"mrcnn_bbox_loss"
:
1.
,
"mrcnn_mask_loss"
:
1.
}
# Use RPN ROIs or externally generated ROIs for training
# Use RPN ROIs or externally generated ROIs for training
# Keep this True for most situations. Set to False if you want to train
# Keep this True for most situations. Set to False if you want to train
# the head branches on ROI generated by code rather than the ROIs from
# the head branches on ROI generated by code rather than the ROIs from
...
...
mrcnn/model.py
浏览文件 @
2a7bcfc8
...
@@ -2122,31 +2122,37 @@ class MaskRCNN():
...
@@ -2122,31 +2122,37 @@ class MaskRCNN():
metrics. Then calls the Keras compile() function.
metrics. Then calls the Keras compile() function.
"""
"""
# Optimizer object
# Optimizer object
optimizer
=
keras
.
optimizers
.
SGD
(
lr
=
learning_rate
,
momentum
=
momentum
,
optimizer
=
keras
.
optimizers
.
SGD
(
clipnorm
=
self
.
config
.
GRADIENT_CLIP_NORM
)
lr
=
learning_rate
,
momentum
=
momentum
,
clipnorm
=
self
.
config
.
GRADIENT_CLIP_NORM
)
# Add Losses
# Add Losses
# First, clear previously set losses to avoid duplication
# First, clear previously set losses to avoid duplication
self
.
keras_model
.
_losses
=
[]
self
.
keras_model
.
_losses
=
[]
self
.
keras_model
.
_per_input_losses
=
{}
self
.
keras_model
.
_per_input_losses
=
{}
loss_names
=
[
"rpn_class_loss"
,
"rpn_bbox_loss"
,
loss_names
=
[
"mrcnn_class_loss"
,
"mrcnn_bbox_loss"
,
"mrcnn_mask_loss"
]
"rpn_class_loss"
,
"rpn_bbox_loss"
,
"mrcnn_class_loss"
,
"mrcnn_bbox_loss"
,
"mrcnn_mask_loss"
]
for
name
in
loss_names
:
for
name
in
loss_names
:
layer
=
self
.
keras_model
.
get_layer
(
name
)
layer
=
self
.
keras_model
.
get_layer
(
name
)
if
layer
.
output
in
self
.
keras_model
.
losses
:
if
layer
.
output
in
self
.
keras_model
.
losses
:
continue
continue
self
.
keras_model
.
add_loss
(
loss
=
(
tf
.
reduce_mean
(
layer
.
output
,
keep_dims
=
True
))
tf
.
reduce_mean
(
layer
.
output
,
keep_dims
=
True
)
*
self
.
config
.
LOSS_WEIGHTS
.
get
(
name
,
1.
))
self
.
keras_model
.
add_loss
(
loss
)
# Add L2 Regularization
# Add L2 Regularization
# Skip gamma and beta weights of batch normalization layers.
# Skip gamma and beta weights of batch normalization layers.
reg_losses
=
[
keras
.
regularizers
.
l2
(
self
.
config
.
WEIGHT_DECAY
)(
w
)
/
tf
.
cast
(
tf
.
size
(
w
),
tf
.
float32
)
reg_losses
=
[
for
w
in
self
.
keras_model
.
trainable_weights
keras
.
regularizers
.
l2
(
self
.
config
.
WEIGHT_DECAY
)(
w
)
/
tf
.
cast
(
tf
.
size
(
w
),
tf
.
float32
)
if
'gamma'
not
in
w
.
name
and
'beta'
not
in
w
.
name
]
for
w
in
self
.
keras_model
.
trainable_weights
if
'gamma'
not
in
w
.
name
and
'beta'
not
in
w
.
name
]
self
.
keras_model
.
add_loss
(
tf
.
add_n
(
reg_losses
))
self
.
keras_model
.
add_loss
(
tf
.
add_n
(
reg_losses
))
# Compile
# Compile
self
.
keras_model
.
compile
(
optimizer
=
optimizer
,
loss
=
[
self
.
keras_model
.
compile
(
None
]
*
len
(
self
.
keras_model
.
outputs
))
optimizer
=
optimizer
,
loss
=
[
None
]
*
len
(
self
.
keras_model
.
outputs
))
# Add metrics for losses
# Add metrics for losses
for
name
in
loss_names
:
for
name
in
loss_names
:
...
@@ -2154,8 +2160,10 @@ class MaskRCNN():
...
@@ -2154,8 +2160,10 @@ class MaskRCNN():
continue
continue
layer
=
self
.
keras_model
.
get_layer
(
name
)
layer
=
self
.
keras_model
.
get_layer
(
name
)
self
.
keras_model
.
metrics_names
.
append
(
name
)
self
.
keras_model
.
metrics_names
.
append
(
name
)
self
.
keras_model
.
metrics_tensors
.
append
(
tf
.
reduce_mean
(
loss
=
(
layer
.
output
,
keep_dims
=
True
))
tf
.
reduce_mean
(
layer
.
output
,
keep_dims
=
True
)
*
self
.
config
.
LOSS_WEIGHTS
.
get
(
name
,
1.
))
self
.
keras_model
.
metrics_tensors
.
append
(
loss
)
def
set_trainable
(
self
,
layer_regex
,
keras_model
=
None
,
indent
=
0
,
verbose
=
1
):
def
set_trainable
(
self
,
layer_regex
,
keras_model
=
None
,
indent
=
0
,
verbose
=
1
):
"""Sets model layers as trainable if their names match
"""Sets model layers as trainable if their names match
...
...
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